There are two primary approaches to constructing models of the human or mammalian brain. A first approach, known as connectionism, consists of constructing interconnected networks of simple elements, and is commonly demonstrated in artificial neural networks. A second approach, known as symbolicism, consists of implementing symbolic logic, and can be found in many industrial systems. Connectionist models effectively implement simple sensory processing; however, to date they have been capable of only limited cognitive processing.
Within the connectionist approach, a central challenge is to relate the incredibly complex behavior of animals to the equally complex activity of their brains. Often the neural elements in these models are not very similar to biological neurons. In other cases, they are quite similar; and may include spiking and other neural dynamics. In both cases, current large-scale neural models have not bridged the gap between neural network activity and biological equivalent function.
Current large-scale models include the Blue Brain Project, which has simulated about a million neurons in cortical columns, and includes significant biological detail accurately reflecting spatial structure, connectivity statistics, and other neural properties. More recent work has simulated many more neurons, such as the Cognitive Computation project, which has simulated one billion neurons.
While impressive scaling has been achieved, no large-scale spiking (or non-spiking) neuron models have demonstrated how such simulations connect to the behavior-switching flexibility of biological systems observable in nature. Unfortunately, simulating a complex brain alone does not explain how complex brain activity generates complex behavior.